Core Concepts of Predictive Marketing: Using Look-alike Targeting

Look-alike targeting is a predictive analytics technique that can help you reach your ideal customer.

Core Concepts of Predictive Marketing

Remarketing only works for known visitors. Remarketing can help you convert more browsers into buyers, and get past buyers to come back and buy again. However, remarketing cannot help you find and acquire new consumers for your products, services or content. This is where look-alike targeting comes in.

Look-alike targeting is a predictive analytics technique to find people who look just like an initial “seed audience.” For example, if you feed a look-alike targeting system, such as Facebook’s look-alike audiences, a list of your existing customers, it can find prospects that have the same characteristics as your existing customers. You can now use this “look-alike audience” to launch an advertising campaign. The principle is not limited to customers. You could feed a look-alike targeting system a seed list of people who have liked your Facebook page, and it will go out and find people who have a high likelihood to also “like” your page. You can also use this principle to find audiences that behave like a specific subset of your customers. For example, perhaps you export a list of your best, most valuable, customers and then advertise only to those prospects who “look like” your best customers. Or perhaps you define a cluster, which likes leather clothing, and now look for prospects that “look like” your leather cluster so you can target very specific advertising featuring leather-clad models to this look-alike audience.

Figure 1 illustrates the concept, using Facebook as an example. Facebook look-alike targeting is increasingly popular but Facebook is not the only network that gives advertisers the ability to use look-alike audiences. Many advertisers, including Twitter, Google Display Network, and others also offer look-alike audience capabilities.

On Facebook, you start by uploading a specific list of customers to Facebook custom audiences. This can be the list of customers who prefer leather products, or perhaps the list of your best customers. Facebook will now try to match these records to its user database. Matching happens based on email address. The list needs to contain at least 100 records that match to a Facebook account. After matching at least 100 users, Facebook now uses their internal algorithms, which also use predictive analytics, to go out and match your segment to other, new people, in the Facebook database that “look like” your original list.

Figure 1: Facebook Look-Alike Targeting

Core Concepts of Predictive Marketing: Using Look-alike Targeting


Look-alike modelling is a powerful tool that enables marketers to go out and target people who have similar traits or behaviours to their existing customers or website visitors. Look-alike algorithms typically need to be fed a list of at least 100 or more existing visitors or customers as a “seed.”

Look-alike audiences can be used to support any business objective: targeting people who are similar to sets of customers for fan acquisition, site registration, purchases, and coupon claims, or simply to drive awareness of a brand. It can also be used to find audiences who put an item in the basket of your website but didn’t pay for it.

Before using look-alike targeting, make sure your seed audience is clean and well selected, or else the look-alike targeting algorithms will not work. Look-alike targeting is only as good as the inputs are. Remember, here, too: garbage in, garbage out. Make sure your seed audience converts really effectively before you expand the seed audience with look-alike targeting. Go for quality before quantity for your seed audience. We recommend that you start with a look-alike campaign that is based on your best customers. These are customers that have bought from you several times and therefore you are sure that these are quality customers.

Optimising for similarity or reach 

Marketers can optimise their look-alike campaigns for “similarity” or “reach.” When optimising for similarity, marketers are looking for impressions with tight accuracy — and presumably better results. You could say, for example, “with 90 percent certainty, I know that this person will buy from you.” There will be fewer of these customers than customers who have, say, a 60 percent chance to buy from you. When optimising for “reach” the match is hazier and the ROI lower, but you might acquire more customers overall.

On Facebook, you can choose to optimise your audience for “similarity” or “reach” automatically or to customise something in between. When optimised for similarity, a look-alike audience will include the top 1 percent of people in the selected country who are most similar to the seed custom audience. The reach of the new audience will be smaller, but the match will be more precise. When optimised for reach, a look-alike audience will include the top 5 percent of people in the selected country that are similar to the seed custom audience, but with a less precise match. Instead of using the types (explained earlier) you can manually set a ratio value that represents the top X percent of the audience in the selected country. The ratio value should be between 1 percent and 20 percent and should be specified in intervals of 1 percent. 

A North American beauty company used product-based clusters to launch specific look-alike advertising campaigns on Facebook. They first uploaded a list of all existing customers who were part of a bath and body cluster. Then they designed the creative for a Facebook advertising campaign specifically to appeal to this type of bath and body customer. This combination of clustering and look-alike targeting turned out to be highly profitable. 

For this beauty company, these campaigns delivered between 2 and 10 times return, when comparing revenues generated to the investment made in Facebook ads. 

As said, look-alike targeting works on many other advertising networks, not just Facebook. The mechanisms for selection and purchasing media are similar across networks and new options become available every year.

To see even more ways that predictive analytics and machine learning can boost your marketing efforts, check out our full Core Concepts of Predictive Marketing series.